Improved compressive tracking based on pixelwise learner

Ting Chen; Hichem Sahli; Yanning Zhang; Tao Yang

doi:10.1117/1.JEI.27.1.013003

5 January 2018 Improved compressive tracking based on pixelwise learner

Ting Chen, Hichem Sahli, Yanning Zhang, Tao Yang

Author Affiliations +

Funded by: Natural Science Foundation of China, CSC-VUB Scholarship, Shenzhen Science and Technology Foundation, Northwestern Polytechnical University (NPU) New Aoxiang Star, Fundamental Resesarch Funds for the Central Universities, Fonds Wetenschappelijk Onderzoek (FWO)

Journal of Electronic Imaging, Vol. 27, Issue 1, 013003 (January 2018). https://doi.org/10.1117/1.JEI.27.1.013003

Abstract

This work expands upon state-of-the-art multiscale tracking based on compressive sensing (CT) by increasing the overall tracking accuracy. A pixelwise classification stage is incorporated in the CT-based tracker to obtain a relatively stable appearance model, by distinguishing object pixels from the background. In addition, we identify potential distracting regions that are used in a feedback strategy to handle occlusion and avoid drifting toward nearby regions with similar appearances. We evaluate our approach on several benchmark datasets to demonstrate its effectiveness with respect to the state-of-the-art tracking algorithms.

Citation Download Citation

Ting Chen, Hichem Sahli, Yanning Zhang, and Tao Yang "Improved compressive tracking based on pixelwise learner," Journal of Electronic Imaging 27(1), 013003 (5 January 2018). https://doi.org/10.1117/1.JEI.27.1.013003

Received: 5 April 2017; Accepted: 4 December 2017; Published: 5 January 2018

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